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        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '/content/' # This can be any directory you want to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "text": [
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Processing...\n",
            "\n",
            "\n",
            "\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mVCuo84ef1b"
      },
      "source": [
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mJmZMHSej1Z"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guWrLqLUelZZ"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "watgH162yyv_"
      },
      "source": [
        "### No Regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zc309_JWem63"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAO3L9MIeqVD"
      },
      "source": [
        "def get_data():     \n",
        "    train = FMNISTDataset(tr_images, tr_targets)     \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets)     \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeptO6C9ert-"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XgKcBFies94"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS71jHNJeuPP",
        "outputId": "ef023469-41e2-495a-efde-b7619006d2d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(10):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kvvJvJ8ew1i",
        "outputId": "87e7efb2-f288-44e6-8905-03cf1aa623bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(10)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lnvd8pUtiGIZhuGDKO3a4xv+77wKVwfbtxfni4uDUU6FvXzITEiLy1V6qgvAWCprvBdl7S0T+DTRQ1X0RkMUwDKPmoApbtoTsEbBnT3G+Jk3gtNPg4ovd3re1bQu13CjB9kWLODUCIpaqIFS1UET+DqR457m4RX8MwzCMcCgsdLFi/BWAr2ewf39xvmbN4PTT4fLLAxVB69Yu/k0UCMfENF9ELgP+z1tcwjAMwwimoAB++unI3sB33wVGoGzZ0jX8f/yj2596qtu3aBE1RVAS4SiI64H/AQ6LSA7O1VVVtUlEJTMMw4gl8vNh5043PrBjhxsP2LChWBGsXQu5fgaWNm1cwz9qVHFv4NRTIT4+aq9QXsKZSd24KgQxDMOoUlSdice/wfcdhzr3HxfwJzHRNf6/+lWxEjj1VGjatEpfJxKUqSBE5PxQ6cELCBmGYUQd/6/8shr8HTsCv/j9adbMmXxatoQzznDHvnPfcYsWrpdw3HFV+45VSDgmpjv9jhsAPYEvgb4RkcgwDMMfVff1/ssvbtu8mfZLlsDs2UcqgL17Q5dRr15g437GGUc29r7zE05w+Y2wTEy/8z8XkbbAlIhJZBhGzeHwYdi2DTZvLmr8/RVB0XFOTsBtHQCaNy9u2H0TxUr60m/SJOYGgKsDYQXrC2IzhOdyKyK3AdfhJtd9A1wNnIObdFcLyAZGquoPInIzbkB8E/B7b2Gic4HLVPW2CshpGOVHFdavh7lzYd48WLgQsrLcpKSGDYv3/seVkebb16177DRkBw6U3uhv3uy++gsLA++rVw8SEpz5pkcPGDTIHbdpU5T+0dq1XDBgQHTeqwYRzhjE33ANPLhGvRtuRnVZ97UBbgFOU9VDIvIGcCXwJ2Cgqn4nIjcA44GRQDpwpnf9196kvPuBYeV9KcMoFzt3woIFTiHMnVu8pF27dq5xatnSuSkeOlS89x3v2wdbtwamHTp0xBdv2NSqVaJCST54EE46CerXd41o/fpHbuVNL+1a7dqhlZUq7Np1ZKMf3PjvCzGf9vjjixv6pKRiReDX+BMfX6aS1B9/rFj9GuUinB7EMr/jw8BrqvpJOcpvKCL5QBywBadsfC6yTb00cO6zdb18+cAfgPdVtQTXgWOHWbN8C+RcQLt2RHeBnCVL4NNP3T9vSgqceGKUBIkghw7Bf/9b3EtYscKlN20KffvC3XdD//5wyikV/5ovLHRKwl9plKRkwkyrtW+fU2a5uZCX5/bB2+HDlVdPIkcqDRH31R88uFurllNebdpA587Qp09go+/bjuEB3WORcBTEm0COqhYAiEhtEYlT1YOl3aSqv4jIZJzJ6BDwoap+KCLXAe+JyCFgP3CWd8szwGfAKuAT4F/AryvyUtWJWbNg9GjfPBph40Z3DlWsJLZtg7vugldeCUxPSIDUVKcsfPuEhOplBikocErA10P45BPXwNWtC+ecA488AgMGuPerUxGrawh8PYG4uErze1+xaFHZCygVFoZWHiUplPKmFxYWKwJ/BXDSSZVXd0bMIGVNjhaRz4D+vpXkvJXlPlTV3mXc1wx4C7eWRCYwG6dsBgN/VdXPReROoIuqXhd07wTga6AQt7zpz8DtqloYlG80MBqgZcuWaRkZGWG9dCxx5ZVnsX17gyPSW7bMISPjs4g/XwoKaD1nDh2mTaNWXh4/DxnCLwMHErd5M43WraPxunU0WreOuE2bEO+3kte0KdmdOpHdqRNZ3v5Q69ZFcWEqi+zsbBo1alShexts2UKzZctotnw5zVasoK4X0iC7Y0f2pqWxt3t3MpOSKGzYsDJFjihHUx/HGlYXgRxNffTp0+dLVe0e8qKqlroBK8NJC5HnCuBFv/PhwHPAer+0dsDqoPtaA//2jj8CagMPAANKe15aWppWR0RUnVE3cBOpgocvXqx65pnugb/6leratSXnzc5WXbJE9ZlnVK+9VjUlRbVu3WKBGzdWPf981VtvVZ0xQ/Wbb1Tz849KvIULF4afedcu1TfeUB09WrVDh2K5EhJUr75addYs1W3bjkqeaFOu+jjGsboI5GjqA1imJbSr4fQJD4hIqqouBxCRNJzJqCw24dayjvPy98ONZ1whIp1V9XtgAPBd0H0PAxO844a4MYtC3NjEMUe7dsVjosHpEWP7dmdnnzHDRYR8800YPLh0s9Fxx8HZZ7vNR14erFoFy5c7E87y5TB1qrOZAzRoAGeeGWieSkpy6UdLTo4zFfnGEZYvdyqhSRNn/779djeO0Llz9TKHGUYMEY6CGAfMFpEtuIHkk3Bmo1JRZ0J6E+fxdBhYAUzFucm+JSKFwF7gGt89IuKLGuvzknoV5x77M/BYmO9UrZg0yX8MwhEX59IrncOH4R//gPHj3QPvvdeNjld04LBePdfop6QUpxUUwPffByqN11+Hf/7TXa9d20Ws9Fca3bpB4zIiuhQWwsqVThnMmweLFzslUacO9O4NDz7oFEKPHmYLN4xKoswxCAARqQt08U7Xqmp+RKWqAN27d9dly5aVnTEGKfZiUtq1k8h4MS1ZAjfe6BrZ/v3hmWegS5ey76sMVF1QM3+lsXx58eInItCpU6DSSEnhs3nzOCs72/US5s+H3btd/jPOcO8wYACcfz7UEFv0onAGqT3y8/PZvHkzORV1t41xcnJyaFAZPdFjhHDqo0GDBiQkJFC3bt2AdBEpcQwinEHqG4FZ6hYN8g0+D1PVZ8shf8SpzgrCR3kagLDZscOZk6ZPdx4nTz4Jl10WG2aXrVsDlcaKFU6RBNO6tVMG/fu72bKtauaKt+X5ffz00080btyY+Ph4JBb+1pVMVlYWjcvqddYgyqoPVWX37t1kZWXRoUOHgGulKYhw+uKjVPXvfg/aKyKjgJhSEEYQBQXF5qTsbKckxo+Pra/tVq3cKlkXX1yctmePUxQrVrBu0yY6jR0LXbvGhkKrRuTk5JCYmHhMKgej/IgI8fHx7Ny5s1z3haMgaos4nxrvQbUBi2QVy3z6qTMnrVjhvrj/9jcXfrg60Ly5k7lfP35ZtIhO1UXuGMSUg+FPRX4P4TiufwC8LiL9RKQf8BrwfrmfZESenTvh2mvdoO327W5weO7c6qMcjGOG3bt3061bN7p168ZJJ51EmzZtis7z8vJKvXfZsmXccsstZT6jd+9Sp2IZlUA4PYi7cZPRxnjnX+M8mYxYoaDAuZf+6U/OnHTnnXD//WV7BhmGR7GjBJUS7iU+Pp6VK1cCMHHiRBo1asQdd9xRdP3w4cPUKcHbrHv37nTvHnrelj9LliypuIBRoqCggNq1a0dbjLApswehbvby58AG3FoQfTly7oIRLT7/HHr2hBtucN4/X30Fjz1mysEIG1+4l40bncOZL9zLrFmV+5yRI0cyZswYevXqxV133cXSpUs5++yzSUlJoXfv3qxduxZwg/GXXHIJ4JTLNddcw4UXXkjHjh15+umni8rzzRz2Dd5ffvnldO3alfT0dN+kW9577z26du1KWloat9xyS1G5/mzYsIHzzjuP1NRUUlNTAxTPX//6V5KSkkhOTuaee+4B4IcffqB///4kJyeTmprK+vXrA2QGuOmmm5g+fToAiYmJ3H333aSmpjJ79myef/55evToQXJyMpdddhkHPR/37du3M2jQIJKTk0lOTmbJkiVMmDCBKVOKV1e47777eOqpp476bxEuJfYgRKQzLpLqMGAX8DqAqvapGtGMUtm1y81jeOEFN9j72mswdKgN5hrl5r77AufhgDu/777Kd7fevHkzS5YsoXbt2uzfv5/FixdTp04d5s2bx5/+9CfeeuutI+5Zs2YNCxcuJCsriy5dujB27Ngj8qxYsYJVq1bRunVrzjnnHD755BO6d+/O9ddfz8cff0yHDh0YNix0YOgWLVowd+5cGjRowLp16xg2bBjLli3j/fff51//+heff/45cXFx7PGWHE1PT+eee+5h0KBB5OTkUFhYyM8//1zqe8fHx7N8uZvetXv3bkaNGgXA+PHjefHFF7n55pu55ZZbuOCCC3j77bcpKCggOzub1q1bM3jwYMaNG0dhYSEZGRksXbq0XHV+NJRmYloDLAYuUdUfoGh9ByOaFBQ4pXDvvW6dgttvhwcesB6DUWE2bSpf+tFwxRVXFJlY9u3bx4gRI1i3bh0iQn5+6OlVF198MfXr16d+/fq0aNGC7du30zRoveeePXuSkJAAQLdu3diwYQONGjWiY8eORW6dw4YNY+rUqUeUn5+fz0033cTKlSupXbs233//PQDz5s3j6quvJi7OBXFo3rw5WVlZ/PLLLwwaNAgg7LkYQ4cWzy3+9ttvGT9+PJmZmWRnZ/PrX7uYpAsWLODll18GoHbt2jRt2pSmTZsSHx/PihUr2L59OykpKcRXUvDHcChNQQzGrd+wUEQ+ADJwM6mNaLF0qfNOWrYMLrzQTXY7/fRoS2VUc6oy3MtxfrP277//fvr06cPbb7/Nhg0bSpzjUb9+/aLj2rVrczhESPNw8pTEk08+ScuWLfnqq68oLCys0AS8OnXqUOi38FHwBEX/9x45ciRz5swhOTmZ6dOns2jRolLLvu6665g+fTrbtm3jmmuuKTVvZVPiGISqzlHVK4GuwEJcyI0WIvKciPyqqgQ0cDOIR4+Gs85yi7G8+qpb4MaUg1EJTJrkwrv4E7FwL37s27ePNm3aABTZ6yuTLl268OOPP7LBm3z5+uuvlyhHq1atqFWrFq+88goFBQUADBgwgGnTphWNEezZs4fGjRuTkJDAnDlzAMjNzeXgwYO0b9+e1atXk5ubS2ZmJvPnzy9RrqysLFq1akV+fj6z/AZ6+vXrx3PPPQe4wex93oJLgwYN4oMPPuCLL74o6m1UFeEMUh9Q1VfVrU2dgIupdHfEJTNc/KGpU13AuZdegttugzVrYNgwG2swKo30dPcza9/e/azat3fnkV6P5K677uLee+8lJSWlXF/84dKwYUOeffZZLrroItLS0mjcuPERpimAG264gRkzZpCcnMyaNWuKvvYvuugiLr30Urp37063bt2YPHkyAK+88gpPP/00Z555Jr1792bbtm20bduWIUOGcMYZZzBkyBBS/OOTBfHwww/Tq1cvzjnnHLp27VqU/tRTT7Fw4UKSkpJIS0tj9erVANSrV48+ffowZMiQqveAKinMa3Xbqmu4b38CQvYuXarao4cLWX3++S58dg3DQjoHUp76WL16deQEiQH2798fVr6srCxVVS0sLNSxY8fqE088EUmxIkJBQYEmJyfr999/X2KecOsj1O+CUsJ9V+4KL9WQWbMgMdGtdZOYWPmufeVm924YMwZ69YKff4aZM2HRIhegzjCMcvH888/TrVs3Tj/9dPbt28f1118fbZHKxerVqznllFPo168fnTp1qvLn1+i4yIHLfRK95T4BCgtp9e67cPnlkJkJt94KEye6dZINw6gQt912G7fdVn2dL0877TR+/PHHqD2/RvcgSvP/rjJU4Z13oFs3ukye7MJiLF/uoq6acjAMI4rUaAVRlf7fIVm40MVNGjgQDh1i9f33w8cfu1XYDMMwokyNVhAl+XlHdLlPcPMZBgyAvn3dOMPUqbB6NTv69jXvJMMwYoYarSCq3P971Sq39nOvXm5ltyeegB9+gFGjIGiVJ8MwjGhToxVElfl///QTDB8OSUluPeUHH4T16928Bls20TgG6dOnD//5z38C0qZMmRIyjpKPCy+8EN+qkL/97W/JzMw8Is/EiROL5iOUxJw5c4rmEABMmDCBefPmlUd8wyOiCkJEbhORVSLyrYi8JiINRGS6iPwkIiu9rZuX9zIv72IRiffSThaR0NMfK4n0dLfKZWGh21eqcti61YXG6NIFZs+GO+5wymLCBGjSpBIfZBixxbBhw8jIyAhIy8jIKDFgXjDvvfcexx9/fIWeHawgHnroIfr371+hsqKFbzZ3tImYghCRNsAtQHdVPQOojYvtBHCnqnbztpVe2s1AD+CfwFVe2iPA+EjJGDH27HFLfJ58suuSXHut6zE89hhUYaAtw4gWl19+Oe+++27R4kAbNmxgy5YtnHfeeYwdO5bu3btz+umn88ADD4S8PzExkV27dgEwadIkOnfuzLCu46QAAAvsSURBVLnnnlsUEhwIGTZ7yZIlvPPOO9x5551069aN9evXM3LkSN58800A5s+fT0pKCklJSVxzzTXk5uYWPe+BBx4gNTWVpKQk1qxZc4RMsRwW/JFHHolIWPBIz4OoAzQUkXwgDthSSt5CoL6XL19EzgO2qeq6CMtYeWRnw5Qp8PjjLtJqerqby3DyydGWzKjJjBvnxrwqk27d3G+9BJo3b07Pnj15//33GThwIBkZGQwZMgQRYdKkSTRv3pyCggL69evH119/zZkleO59+eWXZGRksHLlSg4fPkxqaippaWkADB48OGTY7EsvvZRLLrmEyy+/PKCsnJwcRo4cyfz58+ncuTPDhw/nueeeY9y4cQCccMIJLF++nGeffZbJkyfzwgsvBNwfy2HBmzRpwvDhwys9LHjEehCq+gswGdgEbAX2qeqH3uVJIvK1iDwpIr4wjH8B5gG/wy1rej/wcKTkq1RycuCpp6BjR7eSW58+buGeV14x5WDUWPzNTP7mpTfeeIPU1FRSUlJYtWpVgDkomMWLFzNo0CDi4uJo0qQJl156adG1b7/9lvPOO4+kpCRmzZrFqlWrSpVn7dq1dOjQgc6dOwMwYsQIPv7446LrgwcPBiAtLa0owJ8/+fn5jBo1iqSkJK644ooiucMNCx4X7BETguCw4KHeb8GCBUVjOb6w4O3bty8KC/7hhx9WWljwiPUgRKQZMBDoAGQCs0XkD8C9wDagHjAVF/jvIVWdC8z17h0OvAd0FpE7gL3Arap6MOgZo3HLodKyZcsyw+ZWNlJQQMsPPiDx5ZdpsGMHe1NT+XHiRLJOO82FzCinPNnZ2VX+DrGM1Ucg5amPpk2bkpWV5U4ejtB3lq/8Eujbty/jxo1j8eLFZGdn07lzZ7755hsee+wxFi1aRLNmzRgzZgyZmZlkZWVRUFDAgQMHyMrKQlXJzs4mJyeH3NzconfJy8sjNzeXgoICRowYwauvvlrUgC5evJisrCzy8/M5dOhQ0T2+8wMHDlBQUFCUfvDgQQ4fPlz0vPz8fLKyso54po9HH32UZs2a8d///pfCwkJOPPFEsrKyyMvLIycnJyC/r8zgMvLy8sjLyytK9z3Pl9//npLez5fHf23vgoIC0tPTmTp1Kjt27GDYsGFHPBtcL6pc/1MlBWk62g24AnjR73w48GxQnguBfwelxQELgLrAf4DjgBHAqNKeV6XB+goKVDMyVDt3dsH0evZUnTfvqIu14HSBWH0EUh2D9Q0ZMkSTk5N1woQJqqq6cuVKPfPMM7WgoEC3bdumLVq00GnTpqmq6gUXXKBffPGFqqq2b99ed+7cqV9++aUmJSXpwYMHdf/+/XrKKafo448/rvv379f4+Hjdvn275uXlaf/+/XXEiBGqqnrTTTfpSy+9VCTDiBEjdPbs2Xro0CFt27atrlu3rih9ypQpAc9TVf3iiy/0ggsuOOJdxo0bp5MnT1ZV1Zdeekld86n6/vvv69lnn60HDhxQVdXdu3erqmqvXr307bffVlXVnJwcPXDggG7atEnbt2+vOTk5unfvXk1MTCx6f38ZVLXE9xs6dKg++eSTqqp6+PBhzczM1P3792tubq527txZO3TooIcPHw7594ilYH2bgLNEJE5EBOgHfCcirQC8tN8D3wbddyfwtKrmAw0BxY1PlN0/izSq8N57kJYGV17p5i7MmQOffQb9+kVbOsOIOYYNG8ZXX31VZF5KTk4mJSWFrl27ctVVV3HOOeeUen9qaipDhw4lOTmZ3/zmN/To0aPoWklhs6+88koef/xxUlJSWL9+fVF6gwYNmDZtGldccQVJSUnUqlWLMWPGhP0uNTIseEmaozI24EHc0qXfAq/gBqEXAN94aTOBRn75WwPv+p1fAawCPgFOLO1ZEe9BfPyx6rnnuh5Dx46qM2eqlqClK4p9MQdi9RFIdexBRIpww1vXFPbv3x9WWPDy9iAi6sWkqg8AwX5sfUvJvwW42O98NjA7MtKFyfLlLnrfBx9Aq1bw3HNwzTVQr15UxTIMw/CxZs0ahg4dyqBBgyo1LHiNDvddKmvWOI+kN9+E5s3dHIYbbzwyNodhGEaU6dq1a0TCgpuCCGbjRhcKY8YMpwwmTID/+R8LvW0YRo3DFISP7dvhz3+Gf/zDBWYaNw7uuQdOPDHakhlGhVBVxKIDGx5uuKF8mILIzHQzn6dMgdxcN75w//3Qtm20JTOMCtOgQQN2795NfHy8KQkDVWX37t00KGdwUFMQ69bBX/7i3FYffBCisO6rYVQ2CQkJbN68mZ07d0ZblIiQk5NT7sbuWCac+mjQoAEJCQnlKtcURI8e8OOPkJgYbUkMo9KoW7cuHTp0iLYYEWPRokWlziGoaUSqPmr0ehBFmHIwDMM4AlMQhmEYRkhMQRiGYRghkYq4PsUiIrIT2BhtOY6SE4Bd0RYihrD6CMTqoxiri0COpj7aq2pIf/5jRkEcC4jIMlXtHm05YgWrj0CsPoqxuggkUvVhJibDMAwjJKYgDMMwjJCYgogtpkZbgBjD6iMQq49irC4CiUh92BiEYRiGERLrQRiGYRghMQURA4hIWxFZKCKrRWSViNwabZmijYjUFpEVIvLvaMsSbUTkeBF5U0TWiMh3InJ2tGWKJiJym/d/8q2IvCYiNSook4i8JCI7RORbv7TmIjJXRNZ5+2aV8SxTELHBYeB2VT0NOAu4UUROi7JM0eZW4LtoCxEjPAV8oKpdgWRqcL2ISBvgFqC7qp4B1AaujK5UVc504KKgtHuA+araCZjvnR81piBiAFXdqqrLveMsXAPQJrpSRQ8RScAtPftCtGWJNiLSFDgfeBFAVfNUNTO6UkWdOkBDEakDxAFboixPlaKqHwN7gpIHAjO84xnA7yvjWaYgYgwRSQRSgM+jK0lUmQLcBRRGW5AYoAOwE5jmmdxeEJHjoi1UtFDVX4DJwCZgK7BPVT+MrlQxQUtV3eodbwNaVkahpiBiCBFpBLwFjFPV/dGWJxqIyCXADlX9MtqyxAh1gFTgOVVNAQ5QSeaD6ohnWx+IU5ytgeNE5A/RlSq2UOeaWinuqaYgYgQRqYtTDrNU9f+iLU8UOQe4VEQ2ABlAXxGZGV2RospmYLOq+nqUb+IURk2lP/CTqu5U1Xzg/4DeUZYpFtguIq0AvP2OyijUFEQMIG5NyBeB71T1iWjLE01U9V5VTVDVRNzg4wJVrbFfiKq6DfhZRLp4Sf2A1VEUKdpsAs4SkTjv/6YfNXjQ3o93gBHe8QjgX5VRqCmI2OAc4I+4r+WV3vbbaAtlxAw3A7NE5GugG/DnKMsTNbye1JvAcuAbXBtWo2ZVi8hrwKdAFxHZLCLXAo8CA0RkHa6X9WilPMtmUhuGYRihsB6EYRiGERJTEIZhGEZITEEYhmEYITEFYRiGYYTEFIRhGIYRElMQhlEGIlLg5368UkQqbSaziCT6R+U0jFiiTrQFMIxqwCFV7RZtIQyjqrEehGFUEBHZICKPicg3IrJURE7x0hNFZIGIfC0i80WknZfeUkTeFpGvvM0XIqK2iDzvrXHwoYg09PLf4q0R8rWIZETpNY0ajCkIwyibhkEmpqF+1/apahLwDC4KLcDfgBmqeiYwC3jaS38a+EhVk3HxlFZ56Z2Av6vq6UAmcJmXfg+Q4pUzJlIvZxglYTOpDaMMRCRbVRuFSN8A9FXVH71gi9tUNV5EdgGtVDXfS9+qqieIyE4gQVVz/cpIBOZ6C70gIncDdVX1ERH5AMgG5gBzVDU7wq9qGAFYD8Iwjg4t4bg85PodF1A8Nngx8Hdcb+MLb4Ecw6gyTEEYxtEx1G//qXe8hOJlMNOBxd7xfGAsFK253bSkQkWkFtBWVRcCdwNNgSN6MYYRSeyLxDDKpqGIrPQ7/0BVfa6uzbwoq7nAMC/tZtwKcHfiVoO72ku/FZjqRd8swCmLrYSmNjDTUyICPG1LjRpVjY1BGEYF8cYguqvqrmjLYhiRwExMhmEYRkisB2EYhmGExHoQhmEYRkhMQRiGYRghMQVhGIZhhMQUhGEYhhESUxCGYRhGSExBGIZhGCH5/67P8a4kZq3wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5PhJV4RfHc6",
        "outputId": "e25a5fb9-f17a-4e09-c873-9c542a09a364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5m8km-NPfM8T"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOc9DjsjfM-v"
      },
      "source": [
        "### Regularization - 1e-4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yucge9gfPe7"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)\n",
        "    l1_regularization = 0\n",
        "    for param in model.parameters():\n",
        "      l1_regularization += torch.norm(param,1)\n",
        "    batch_loss = loss_fn(prediction, y) + 0.0001*l1_regularization\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NTiKDPZgWs8"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model_l1, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "473oc4g0gaKq",
        "outputId": "bfce7740-f85d-4200-c0b3-6dc8a112f786",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(30):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model_l1, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model_l1)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model_l1)\n",
        "        validation_loss = val_loss(x, y, model_l1)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n",
            "21\n",
            "22\n",
            "23\n",
            "24\n",
            "25\n",
            "26\n",
            "27\n",
            "28\n",
            "29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g6gqU7dgb89",
        "outputId": "a7ec522e-a117-4ad7-d5c9-24d0bf975793",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(30)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with L1 regularization')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with L1 regularization')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQpc_bIxgvo8",
        "outputId": "7acc9fb9-ef06-4f75-ccf7-63a2b832d44a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kK1KXCExyywl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VSc2FkBhpU5"
      },
      "source": [
        "### Regularization 1e-2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stPdysFphpXr"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)\n",
        "    l2_regularization = 0\n",
        "    for param in model.parameters():\n",
        "      l2_regularization += torch.norm(param,2)\n",
        "    batch_loss = loss_fn(prediction, y) + 0.01*l2_regularization\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poLmwOqohvJL"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model_l2, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWLYUYVMhxNT",
        "outputId": "eafaa657-0e23-4cfc-e45a-8269b3ef76a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(30):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model_l2, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model_l2)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model_l2)\n",
        "        validation_loss = val_loss(x, y, model_l2)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n",
            "21\n",
            "22\n",
            "23\n",
            "24\n",
            "25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieeGRz04h2Re"
      },
      "source": [
        "epochs = np.arange(30)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with L2 regularization')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with L2 regularization')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLFTzhJbiNQY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndswNmBBr4qX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIIHtFdSr4tB"
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYXpuDbGsfDv"
      },
      "source": [
        "for ix, par in enumerate(model_l1.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVLaPXe0slWV"
      },
      "source": [
        "par.cpu().detach().numpy().flatten()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qM6Q5XRtsv61"
      },
      "source": [
        "for ix, par in enumerate(model_l2.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9QBeqRws4ka"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}